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Inference Rules for Fuzzy Functional Dependencies in Possibilistic Databases

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Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery (BDAS 2015, BDAS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

Abstract

We consider fuzzy functional dependencies (FFDs) which can exist between attributes in possibilistic databases. The degree of FFD is evaluated by two numbers from the unit interval which correspond to possibility and necessity measures. The notion of FFD is defined with the use of the extended Gödel implication operator. For such dependencies we present inference rules as a fuzzy extension of Armstrong’s axioms. We show that they form a sound and complete system.

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Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, Lodz, Poland

    Krzysztof Myszkorowski

Authors
  1. Krzysztof Myszkorowski
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Correspondence to Krzysztof Myszkorowski .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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© 2016 Springer International Publishing Switzerland

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Myszkorowski, K. (2016). Inference Rules for Fuzzy Functional Dependencies in Possibilistic Databases. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-34099-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34098-2

  • Online ISBN: 978-3-319-34099-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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